Error using trainNetwork (line 184) Invalid training data. For regression tasks, responses must be a vector, a matrix, or a 4-D array of numeric responses. Responses must not

clc; clear all; close all;
%Import/Upload data
load generated_data.mat
%transposing glucose data
X1_T = X1';
%transposing insulin data
X2_T = X2';
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
%DataParts = zeros(size(Train_inputX1,1), size(Train_inputX1,2),1,2); %(4500,400,1,2)
%DataParts(:,:,:,1) = real(cell2mat(Train_inputX1));
%DataParts(:,:,:,2) = imag(cell2mat(Train_inputX1)) ;
XTrain=(reshape(train_X1', [2289,1,1,120])); %Train data
%Separating and partioning for validation data
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,1,1,30])); %Train data
%Separating and partioning for test data
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,1,1,30])); %Train data
%Xtest=(reshape(test_X1, [120,1,1,2289])); %Train data
%Separating data in training, validation and testing data
%X2_train = X2_T;
%Partioning data for training
%train_X2 = X2_train(1:120,:);
%Separating and partioning for validation data
%val_X2 = X2_train(121:150,:);
%Separating and partioning for test data
%test_X2 = X2_train(151:180,:);
%The number of features chosen to be two representing both glucose and
%insulin
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([2289 1 1])  % Creating the image layer
    convolution2dLayer([102 1],3,'Stride',1)
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    %     convolution2dLayer([24 1],10,'Stride',1)
    %     maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    %    convolution2dLayer([11 1],10,'Stride',1)
    %     maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    %     convolution2dLayer([9 1],10,'Stride',1)
    %     maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    %fullyConnectedLayer(6)
    %fullyConnectedLayer(6)
    fullyConnectedLayer(6)
    softmaxLayer
    regressionLayer];
% Specify training options.
opts = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XVal,categorical(val_Y1)},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = categorical(train_Y1);
net1 = trainNetwork(XTrain,yc,layers,opts);

Error using trainNetwork (line 184)
Invalid training data. For regression tasks, responses must be a vector, a matrix, or a 4-D array of numeric responses. Responses must not contain NaNs.

%% Compare against testing Data
miniBatchSize =27;
YPred = classify(net1,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
acc = mean(YPred(:) == categorical(test_Y1(:)))
figure
t = confusionchart(categorical(test_Y1(:)),YPred(:));

[Walter Roberson]

Categorical data is not numeric.

Answer 1

[yanqi liu]

yes，sir，may be use label change to generate double Y，and train as regress application，such as

clc; clear all; close all;
%Import/Upload data
load generated_data.mat
% change to label vector
CS = categories(categorical(Y1));
Z1 = []; Z2 = [];
for i = 1 : length(Y1)
    Z1(i,1) = find(Y1(i)==CS);
end
for i = 1 : length(Y2)
    Z2(i,1) = find(Y2(i)==CS);
end
Yo1 = Y1;
Yo2 = Y2;
Y1 = Z1;
Y2 = Z2;
%transposing glucose data
X1_T = X1';
%transposing insulin data
X2_T = X2';
rand('seed', 0)
ind = randperm(size(X1_T, 1));
X1_T = X1_T(ind, :);
Y1 = Y1(ind);
%Separating data in training, validation and testing data
X1_train = X1_T;
%Partioning data for training
train_X1 = X1_train(1:120,:);
train_Y1 = Y1(1:120);
%DataParts = zeros(size(Train_inputX1,1), size(Train_inputX1,2),1,2); %(4500,400,1,2)
%DataParts(:,:,:,1) = real(cell2mat(Train_inputX1));
%DataParts(:,:,:,2) = imag(cell2mat(Train_inputX1)) ;
XTrain=(reshape(train_X1', [2289,1,1,120])); %Train data
%Separating and partioning for validation data
val_X1 = X1_train(121:150,:);
val_Y1 = Y1(121:150);
XVal=(reshape(val_X1', [2289,1,1,30])); %Train data
%Separating and partioning for test data
test_X1 = X1_train(151:180,:);
test_Y1 = Y1(151:180);
XTest=(reshape(test_X1', [2289,1,1,30])); %Train data
%% NETWORK ARCHITECTURE
layers = [imageInputLayer([2289 1 1])  % Creating the image layer
 convolution2dLayer([102 1],3,'Stride',1)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
    dropoutLayer
    fullyConnectedLayer(1)
    regressionLayer];
% Specify training options.
opts = trainingOptions('adam', ...
    'MaxEpochs',1500, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XVal,val_Y1},...
    'ExecutionEnvironment', 'cpu', ...
    'ValidationPatience',Inf);
%% Train network
%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);
yc = train_Y1(:);
net1 = trainNetwork(XTrain,yc,layers,opts);
%% Compare against testing Data
miniBatchSize =27;
YPred = predict(net1,XTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
predictionError = YPred(:) - test_Y1(:);
thr = 1;
numCorrect = sum(abs(predictionError) < thr);
numValidationImages = numel(YPred);
accuracy = numCorrect/numValidationImages

accuracy =

0.9333

>>

[l]

Hello,I have the same problem, but it hasn't been solved yet , so I hope you can give me some advice.My code is as follows:

gwsdata = reshape(gwsdata,205,3627);

tempdata = reshape(tempdata,205,3627);

ndvidata = reshape(ndvidata,205,3627);

precdata = reshape(precdata,205,3627);

petdata = reshape(petdata,205,3627);

lstdata = reshape(lstdata,205,3627);

smdata = reshape(smdata,205,3627);

%划分训练集，验证集

[trainInd, testInd] = dividerand(205, 0.8, 0.2); % 80%的数据作为训练集，20%的数据作为测试集

temp_train = reshape(tempdata(trainInd, :), 39, 93, 1, 142);

ndvi_train = reshape(ndvidata(trainInd, :), 39, 93, 1, 142);

prec_train = reshape(precdata(trainInd, :), 39, 93, 1, 142);

pet_train = reshape(petdata(trainInd, :), 39, 93, 1, 142);

lst_train = reshape(lstdata(trainInd, :), 39, 93, 1, 142);

sm_train = reshape(smdata(trainInd, :), 39, 93, 1, 142);

gws_train = reshape(gwsdata(trainInd, :), 39, 93, 1, 142);

temp_test = reshape(tempdata(testInd, :), 39, 93, 1, 36);

ndvi_test = reshape(ndvidata(testInd, :), 39, 93, 1, 36);

prec_test = reshape(precdata(testInd, :), 39, 93, 1, 36);

pet_test = reshape(petdata(testInd, :), 39, 93, 1, 36);

lst_test = reshape(lstdata(testInd, :), 39, 93, 1, 36);

sm_test = reshape(smdata(testInd, :), 39, 93, 1, 36);

gws_test = reshape(gwsdata(testInd, :), 39, 93, 1, 36);

%数据归一化

temp_train_norm = normalize(temp_train);

temp_test_norm = normalize(temp_test);

%ndvi_train_norm = normalize(ndvi_train);

%ndvi_test_norm = normalize(ndvi_test);

prec_train_norm = normalize(prec_train);

prec_test_norm = normalize(prec_test);

pet_train_norm = normalize(pet_train);

pet_test_norm = normalize(pet_test);

lst_train_norm = normalize(lst_train);

lst_test_norm = normalize(lst_test);

sm_train_norm = normalize(sm_train);

sm_test_norm = normalize(sm_test);

%gws_train_norm = normalize(gws_train);

%gws_test_norm = normalize(gws_test);

% 构造网络结构

layers = [

imageInputLayer([39 93 6],"Name","imageinput")

convolution2dLayer([3 3],50,"Name","conv2d_1","Padding",[1 1 1 1])

reluLayer("Name","Relu_1")

maxPooling2dLayer([2 2],"Name","pool_1","Padding","same","Stride",[2 2])

dropoutLayer(0.2,"Name","dropout_1")

convolution2dLayer([3 3],100,"Name","conv2d_2","Padding",[1 1 1 1])

reluLayer("Name","Relu_2")

maxPooling2dLayer([2 2],"Name","pool_2","Padding","same","Stride",[2 2])

dropoutLayer(0.2,"Name","dropout_2")

convolution2dLayer([3 3],200,"Name","conv2d_3","Padding",[1 1 1 1])

reluLayer("Name","Relu_3")

maxPooling2dLayer([2 2],"Name","pool_3","Padding","same","Stride",[2 2])

dropoutLayer(0.3,"Name","dropout_3")

convolution2dLayer([3 3],300,"Name","conv2d_4","Padding",[1 1 1 1])

reluLayer("Name","Relu_4")

fullyConnectedLayer(200,"Name","fc_1")

reluLayer("Name","relu_5")

dropoutLayer(0.4,"Name","dropout_4")

fullyConnectedLayer(1164*2783,"Name","fc_2")

regressionLayer("Name","Output")];

lgraph = layerGraph(layers);

options = trainingOptions('adam', ...

'MaxEpochs', 32, ...

'MiniBatchSize', 15, ...

'Shuffle', 'every-epoch', ...

'InitialLearnRate', 1e-2, ...

'LearnRateDropFactor', 0.01, ...

'LearnRateDropPeriod', 10, ...

'LearnRateSchedule','piecewise',...

'Plots', 'training-progress',...

'ExecutionEnvironment', 'cpu', ...

"L2Regularization", 0.001,...

'Verbose', true);

net = trainNetwork({temp_train, ndvi_train, prec_train,pet_train,sm_train,lst_train}, gws_train, lgraph, options);

Error for trainnetwork:Invalid training data. The predictor and response must have the same number of observations.

But when I checked the data, I found that the data seemed to be correct. Could you tell me why? Thanks a lot!